Forward Returns – (Next Month Start)This indicator calculates 1-month, 3-month, 6-month, and 12-month forward returns starting from the first trading day of the month following a defined price event.
A price event occurs when the selected asset drops below a user-defined threshold over a chosen timeframe (Day, Week, or Month).
For monthly conditions, the script evaluates the entire performance of the previous calendar month and triggers the event only at the first trading session of the next month, ensuring accurate forward-return alignment with historical monthly cycles.
The forward returns for each detected event are displayed in a paginated performance table, allowing users to navigate through large datasets using a page selector. Each page includes:
Entry Date
Forward returns (1M, 3M, 6M, 12M)
Average forward return
Win rate (percentage of positive outcomes)
This tool is useful for studying historical performance after major drawdowns, identifying seasonal patterns, and building evidence-based risk-management or timing models.
Statistics
Frank Strategy V2.06 Quantum FilterThe Frank Strategy indicator version 2.06 is designed to:
Identify high-probability entries
Filter out false signals typical of XAUUSD (especially M1–M5)
Enter only when trend + momentum + market coherence are aligned
Provide automatic TP/SL based on volatility
Get additional confirmation with the quant filter
It is a strategy for short and medium-term trends, not for impulsive scalping or excessively long cycles.
The Frank Strategy aims to:
Do not chase the price
Do not enter sideways
Do not trade without momentum
Do not trade without coherence between trend + strength + volatility
Avoid impulsive and noisy entries
It is a strategy designed to be:
selective
precise
repeatable
disciplined
Liquidation Cascade Detector [QuantAlgo]🟢 Overview
The Liquidation Cascade Detector employs multi-dimensional microstructure analysis to identify forced liquidation events by synthesizing volume anomalies, price acceleration dynamics, and volatility regime shifts. Unlike conventional momentum indicators that merely track directional bias, this indicator isolates the specific market conditions where leveraged positions experience forced unwinding, creating asymmetric opportunities for mean reversion traders and market makers to take advantage of temporary liquidity imbalances.
These liquidation cascades manifest through various catalysts: overwhelming spot selling coupled with leveraged long liquidation forced unwinding creates downward spirals where organic sell pressure triggers margin calls, which generate additional selling that triggers more margin calls. Conversely, sudden large buy orders or coordinated buying can squeeze overleveraged shorts, forcing buy-to-cover orders that push price higher, triggering additional short stops in a self-reinforcing feedback loop. The indicator captures both scenarios, regardless of whether the initial catalyst is organic flow or forced liquidation.
For sophisticated traders/market makers deploying amplification strategies, this indicator serves as an early warning system for distressed order flow. By detecting the moments when cascading stop-losses and margin calls create self-reinforcing price movements, the system enables traders to: (1) identify forced participants experiencing capital pressure, (2) strategically add liquidity in the direction of panic flow to amplify displacement, (3) accumulate contra-positions during the overshoot phase, and (4) capture mean reversion profits as equilibrium pricing reasserts itself. This approach transforms destructive liquidation events into potential profit opportunities by systematically front-running and then fading coordinated forced selling/buying.
🟢 How It Works
The detection engine operates through a three-tier confirmation framework that validates liquidation events only when multiple independent market stress indicators align simultaneously:
► Tier 1: Volume Anomaly Detection
The system calculates bar-to-bar volume ratios to identify abnormal participation spikes characteristic of forced liquidations. The Volume Spike threshold filters for transactions where current volume significantly exceeds previous bar volume. When leveraged positions hit stop-losses or margin requirements, their simultaneous unwinding creates distinctive volume signatures absent during organic price discovery. This metric isolates moments when market makers face one-sided order flow from distressed participants unable to control execution timing, whether triggered by whale orders absorbing liquidity or cascading margin calls creating relentless directional pressure.
► Tier 2: Price Acceleration Measurement
By comparing current bar's absolute body size against the previous bar's movement, the algorithm quantifies momentum acceleration. The Price Acceleration threshold identifies scenarios where price velocity increases dramatically, a hallmark of cascading liquidations where each stop-loss triggers additional stops in a feedback loop. This calculation distinguishes between gradual trend development (irrelevant for amplification attacks) and explosive moves driven by forced order flow requiring immediate liquidity provision. The metric captures both panic selling scenarios where spot sellers overwhelm bid liquidity triggering long liquidations, and short squeeze dynamics where aggressive buying exhausts offer-side depth forcing short covering.
► Tier 3: Volatility Expansion Analysis
The indicator measures bar range expansion by computing the current high-low range relative to the previous bar. The Volatility Spike threshold captures regime shifts where intrabar price action becomes erratic, evidence that market depth has evaporated and order book imbalance is driving price. Combined with body-to-range analysis indicating strong directional conviction, this metric confirms that volatility expansion reflects genuine liquidation pressure rather than random noise or low-volume chop.
*Supplementary Confirmation Metrics
Beyond the three primary detection tiers, the system analyzes additional candle characteristics that distinguish genuine liquidation events from ordinary volatility:
► Candle Strength: Measures the ratio of candle body size to total bar range. High readings (above 60%) indicate strong directional conviction where price moved decisively in one direction with minimal retracement. During liquidations, distressed traders execute market orders that drive price aggressively without the normal back-and-forth of balanced trading. Strong-bodied candles with minimal wicks confirm forced participants are accepting any available price rather than attempting to minimize slippage, validating that observed volume and price acceleration stem from liquidation pressure rather than routine trading.
► Volume Climax: Identifies when current volume reaches the highest level within recent history. Climax volume events mark terminal liquidation phases where maximum panic or squeeze intensity occurs. These extreme participation spikes typically represent the final wave of forced exits as the last remaining stops are triggered or the final shorts capitulate. For mean reversion traders, volume climax signals provide optimal reversal entry timing, as they mark maximum displacement from equilibrium when all forced sellers/buyers have been exhausted.
*Directional Classification
The system categorizes cascades into two actionable classes:
1. Short Liquidation (Bullish Cascade): Upward price movement combined with cascade patterns equals forced short covering. This occurs when aggressive spot buying (often from whales placing large market orders) or coordinated buy programs exhaust available offer liquidity, spiking price upward and triggering clustered short stop-losses. Short sellers experiencing margin pressure must buy-to-close regardless of price, creating artificial demand spikes that compound the initial buying pressure. The combination of organic buying and forced covering creates explosive upward moves as each liquidated short adds buy-side pressure, triggering additional shorts in a self-reinforcing loop. Market makers can amplify this by lifting offers ahead of forced buy orders, then selling into the exhaustion at elevated levels.
2. Long Liquidation (Bearish Cascade): Downward price movement combined with cascade patterns equals forced long liquidation. This manifests when heavy spot selling (panic sellers, large institutional unwinds, or coordinated distribution) overwhelms bid-side liquidity, breaking through support levels where long stop-losses cluster. Over-leveraged longs facing margin calls must sell-to-close at any price, generating artificial supply waves that compound the initial selling pressure. The dual force of organic selling coupled with forced long liquidation creates downward spirals where each margin call triggers additional margin calls through further price deterioration. Amplification opportunities exist by hitting bids ahead of panic selling, accumulating long positions during the capitulation, and reversing as sellers exhaust.
🟢 How to Use
1. For Mean Reversion Traders
When the indicator highlights a short liquidation cascade (green background), this signals that shorts are experiencing forced buy-to-cover pressure, often initiated by whale bids or aggressive spot buying that triggered the squeeze. Mean reversion traders can interpret this as a temporary upward dislocation from fair value. As the dashboard shows declining momentum metrics and the cascade highlighting stops, this represents a potential fade opportunity. Enter short positions expecting price to revert back toward pre-cascade levels once the forced buying exhausts and the initial large buyer completes their accumulation.
When a long liquidation cascade triggers (red background), longs are undergoing forced sell-to-close liquidation, typically catalyzed by overwhelming spot selling that breached key support levels. This creates artificial downward pressure disconnected from fundamental value, as margin-driven forced selling compounds organic sell flow. Mean reversion traders wait for the cascade to complete (dashboard transitions from active liquidation status to neutral), then enter long positions anticipating snap-back toward equilibrium pricing as panic subsides and forced sellers are exhausted.
You can also monitor the dashboard's Volume Climax indicator. When it displays "YES" during an active cascade, this suggests the liquidation is reaching its terminal phase, whether driven by the final shorts being squeezed out or the last leveraged longs capitulating. Mean reversion entries become highest probability at this point, as maximum displacement from fair value has occurred. Wait for the next 1-3 bars after climax confirmation, then enter contra-trend positions with tight stops.
The Candle Strength metric also helps validate entry timing. When candle strength readings drop significantly after maintaining elevated levels during the cascade, this divergence indicates absorption is occurring. Market makers are stepping in to provide liquidity, supporting your mean reversion thesis. Strong candle bodies during the cascade followed by weaker bodies signal the forced flow is diminishing.
2. For Momentum & Trend Following Traders
When price breaks through a significant resistance level and immediately triggers a short liquidation cascade (green background), this confirms breakout validity through forced participation. Shorts positioned against the breakout are now experiencing margin pressure from the combination of breakout momentum and potential whale buying, creating self-reinforcing buying that propels price higher. Enter long positions during the cascade or immediately after, as the forced covering provides fuel for extended momentum continuation.
Conversely, when price breaks below key support and triggers a long liquidation cascade (red background), the breakdown is validated by forced selling from trapped longs. Heavy spot selling coupled with margin liquidations creates accelerated downside momentum as liquidations cascade through clustered stop-loss levels. Enter short positions as the cascade develops, riding the combined force of organic selling and forced liquidation for extended trend moves.
3. For Sophisticated Traders & Market Makers
► Amplification Attack Execution
Sophisticated operators can exploit cascades through systematic amplification positioning. When a short liquidation is detected (green highlight activating), often initiated by whale bids absorbing offer liquidity, place aggressive buy orders to front-run and amplify the forced short covering. This exacerbates upward pressure, pushing price further from equilibrium and triggering additional clustered stops. Simultaneously begin accumulating short positions at these artificially elevated levels. As dashboard metrics indicate cascade exhaustion (volume spike declining, climax signal appearing, candle strength weakening), flatten amplification longs and hold accumulated shorts into the mean reversion.
For long liquidations (red highlight), typically catalyzed by heavy spot selling overwhelming bid depth, execute the inverse strategy. Place aggressive sell orders to compound the panic selling, amplifying downward displacement and accelerating margin call triggers. Layer long entries at depressed prices during this amplification phase as forced liquidation selling creates artificial supply. When dashboard signals cascade completion (metrics normalizing, volume climax passing), exit amplification shorts and maintain long positions for the reversal trade.
► Market Making During Liquidity Crises
During detected cascades, temporarily adjust quote placement strategy. When dashboard shows all three confirmation metrics activating simultaneously with strong candle bodies, this indicates the highest probability liquidation event, whether from whale order flow or cascading margin calls. Widen spreads dramatically to capture enhanced edge during the liquidity vacuum. Alternatively, step away from quote provision entirely on your natural inventory side (stop offering during short cascades driven by aggressive buying, stop bidding during long cascades driven by overwhelming selling) to avoid adverse selection from forced flow.
Use cascade detection to inform inventory management. During short cascades initiated by large buy orders or short squeezes, reduce existing short inventory exposure while allowing the forced buying to push price higher. Rebuild short inventory only at the inflated levels created by liquidation pressure. During long cascades where spot selling compounds leveraged liquidation, reduce long inventory and use the forced selling to reaccumulate at artificially depressed prices rather than providing stabilizing liquidity too early.
► Sequential Positioning Strategy
Advanced traders can structure trades in phases: (1) Initial amplification orders placed immediately upon cascade detection to front-run forced flow, (2) Contra-position accumulation scaled in as displacement extends and dashboard readings intensify, (3) Amplification trade exit when metrics show deceleration or candle strength weakens, (4) Contra-position hold through mean reversion, targeting pre-cascade price levels. This sequential approach extracts profit from both the dislocation phase and the subsequent equilibrium restoration.
► Risk Monitoring
If cascade highlighting persists across many consecutive bars while dashboard volume readings remain extremely elevated with sustained strong candle bodies, this suggests sustained institutional deleveraging or persistent whale activity rather than simple retail liquidation. Reduce amplification position sizing significantly, as these extended events can exhibit delayed mean reversion. Professional counter-parties may be establishing dominant positions, limiting your edge.
When volatility spike metrics decline while cascade highlighting continues, professional absorption is occurring. Proceed cautiously with amplification strategies, as intelligent liquidity providers are already positioning for the reversal, potentially front-running your intended reversal trade. Similarly, if large liquidation wicks appear during cascades, this indicates partial absorption is happening, suggesting more sophisticated players are taking the opposite side of distressed flow.
52-Week High Drawdown (Events, Freq & Current)52-Week High Drawdown - Events, Freq & Current
OVERVIEW
Track and analyze drawdowns from 52-week highs with comprehensive statistics on drawdown events, frequency, and current market positioning. Perfect for risk management, historical analysis, and understanding volatility patterns.
KEY FEATURES
📊 Real-Time Drawdown Tracking
Visual area chart showing current intraday maximum drawdown from rolling high
Automatically plots depth below zero line for easy interpretation
Color-coded reference lines at -10% and -20% levels
📈 Event-Based Historical Analysis
Automatically categorizes drawdown cycles across four severity zones:
5-10% Drawdowns - Minor corrections
10-15% Drawdowns - Moderate pullbacks
15-20% Drawdowns - Significant corrections
20%+ Drawdowns - Major corrections/bear markets
⏱️ Frequency Metrics
Calculates average time between events for each category, displayed as "Every X months" to understand typical correction patterns.
🎯 Current Cycle Tracking
Real-time display of maximum drawdown depth in the current cycle, helping you gauge present market position.
📅 Smart Timeframe Adaptation
Auto-Adjust Mode: Automatically selects optimal lookback (Daily=252, Weekly=52, Monthly=12)
Manual Mode: Set custom lookback period for specialized analysis
HOW IT WORKS
The indicator identifies drawdown cycles - periods from one high to the next. When price touches a new rolling high, the previous cycle ends and is categorized by its maximum depth.
Cycle Logic:
Tracks deepest point reached since last high
When price touches/exceeds rolling high, cycle completes
Cycle categorized into appropriate drawdown zone
New cycle begins
This provides accurate event counting without double-counting fluctuations within larger drawdowns.
PRACTICAL APPLICATIONS
Risk Management
Understand typical drawdown patterns for position sizing
Set realistic stop-loss levels based on historical norms
Anticipate potential correction depths during bull markets
Market Context
Identify when current drawdowns are extreme vs. typical
Compare across different assets and timeframes
Historical perspective during volatile periods
Strategic Planning
Time entries during typical correction zones
Recognize when drawdowns exceed historical norms
Build resilience strategies based on frequency data
SETTINGS GUIDE
Auto-Adjust Lookback by Timeframe
Checked: Automatically uses appropriate period for chart timeframe
Unchecked: Uses manual lookback value
Manual Lookback Length
Default: 252 (trading days in a year)
Customize for specific analysis periods
Higher values = longer historical perspective
Table Position
Choose from Top Right, Bottom Right, Top Left, or Bottom Left based on your chart layout.
INTERPRETATION TIPS
Frequency data becomes more reliable with longer history (5+ years ideal)
"Never" frequency indicates zero events in available data range
Current Cycle Max shows 0.00% at new highs, otherwise displays deepest point
Compare frequencies across assets to understand relative volatility profiles
BEST USED FOR
Stocks, ETFs, and Indices with sufficient historical data
Long-term investing and swing trading strategies
Portfolio risk assessment and stress testing
Educational purposes - understanding market behavior
Multi-timeframe analysis (daily, weekly, monthly)
TECHNICAL NOTES
Uses ta.highest() for efficient rolling high calculation
Event detection logic prevents double-counting
Frequency calculated from actual data start time to present
All calculations update in real-time with each new bar
💡 Tip: Run this indicator on major indices like SPY or QQQ with maximum available history to build a comprehensive baseline for equity market corrections.
Created to provide institutional-grade drawdown analysis in an accessible format. Free to use and modify.
Alpha V3 proAlpha V3 pro is a custom technical indicator designed specifically for binary options trading. It analyzes market structure, price action behavior, and momentum shifts to generate high-probability buy and sell signals. The indicator filters out noise and focuses on identifying clear market reversals or trend continuations, helping traders take more accurate entries within short-term timeframes. With its optimized signal logic, Alpha V3 aims to provide timely alerts, improved decision-making, and greater consistency for traders looking to capitalize on fast binary option opportunities.
MTF-SumTabThis is Summary Table of different Time Frames, and this gives an insight into the Trend...
SVE Daily ATR + SDTR Context BandsSVE Daily ATR + SDTR Context Bands is a free companion overlay from The Volatility Engine™ ecosystem.
It plots daily ATR-based expansion levels and a Standardized Deviation Threshold Range (SDTR) to give traders a clean, quantitative view of where intraday price sits relative to typical daily movement and volatility extremes.
This module is designed as an SVE-compatible context layer—using discrete, RTH-aligned daily zones, expected-move bands, and a standardized volatility shell—so traders can build situational awareness even without the full SPX Volatility Engine™ (SVE).
It does not generate trade signals.
Its sole purpose is to provide a clear volatility framework you can combine with your own structure, Fibonacci, or signal logic (including SVE, if you use it).
🔍 What It Shows
* Daily ATR Bands (expHigh / expLow)
- Expected high/low based on smoothed daily ATR
- Updates at the RTH open
* Daily SDTR Bands (expHighSDTR / expLowSDTR)
- Standard deviation threshold range for volatility extremes
- Helps identify overextended conditions
Discrete RTH-aligned Zones
- Bands reset cleanly at each RTH session
No continuous carry-over from prior days
Daily ATR & SDTR stats label
Quick-reference box showing current ATR and SDTR values
🎯 Purpose
This tool helps traders:
- Gauge intraday context relative to expected daily movement
- Assess volatility state (quiet, normal, expanded, extreme)
- Identify likely exhaustion or expansion zones
- Frame intraday price action inside daily volatility rails
- Support decision-making with objective context rather than emotion
It complements any strategy and works on any intraday timeframe.
⚙️ Inputs
- ATR Lookback (default: 20 days)
- RTH Session Times
- SDTR Lookback
- Show/Hide Daily Stats Label
🧩 Part of the SVE Ecosystem
This module is part of the broader SPX Volatility Engine™ framework.
The full SVE system includes:
- Composite signal scoring
- Volatility compression logic
- Histogram slope and momentum analysis
- Internals (VIX / VVIX / TICK)
- Structural zone awareness
- Real-time bias selection
- High-clarity decision support
⚠️ Disclaimer
This tool is provided for educational and informational purposes only.
No performance claims are made or implied.
Not investment advice.
Chronos Reversal Labs - SPChronos Reversal Labs - Shadow Portfolio
Chronos Reversal Labs - Shadow Portfolio: combines reinforcement learning optimization with adaptive confluence detection through a shadow portfolio system. Unlike traditional indicator mashups that force traders to manually interpret conflicting signals, this system deploys 4 multi-armed bandit algorithms to automatically discover which of 5 specialized confluence strategies performs best in current market conditions, then validates those discoveries through parallel shadow portfolios that track virtual P&L for each strategy independently.
Core Innovation: Rather than relying on static indicator combinations, this system implements Thompson Sampling (Bayesian multi-armed bandits), contextual bandits (regime-specific learning), advanced chop zone detection (geometric pattern analysis), and historical pre-training to build a self-improving confluence detection engine. The shadow portfolio system runs 5 parallel virtual trading accounts—one per strategy—allowing the system to learn which confluence approach works best through actual position tracking with realistic exits.
Target Users: Intermediate to advanced traders seeking systematic reversal signals with mathematical rigor. Suitable for swing trading and day trading across stocks, forex, crypto, and futures on liquid instruments. Requires understanding of basic technical analysis and willingness to allow 50-100 bars for initial learning.
Why These Components Are Combined
The Fundamental Problem
No single confluence method works consistently across all market regimes. Kernel-based methods (entropy, DFA) excel during predictable phases but fail in chaos. Structure-based methods (harmonics, BOS) work during clear swings but fail in ranging conditions. Technical methods (RSI, MACD, divergence) provide reliable signals in trends but generate false signals during consolidation.
Traditional solutions force traders to either manually switch between methods (slow, error-prone) or interpret all signals simultaneously (cognitive overload). Both fail because they assume the trader knows which regime the market is in and which method works best.
The Solution: Meta-Learning Through Reinforcement Learning
This system solves the problem through automated strategy selection : Deploy 5 specialized confluence strategies designed for different market conditions, track their real-world performance through shadow portfolios, then use multi-armed bandit algorithms to automatically select the optimal strategy for the next trade.
Why Shadow Portfolios? Traditional bandit implementations use abstract "rewards." Shadow portfolios provide realistic performance measurement : Each strategy gets a virtual trading account with actual position tracking, stop-loss management, take-profit targets, and maximum holding periods. This creates risk-adjusted learning where strategies are evaluated on P&L, win rate, and drawdown—not arbitrary scores.
The Five Confluence Strategies
The system deploys 5 orthogonal strategies with different weighting schemes optimized for specific market conditions:
Strategy 1: Kernel-Dominant (Entropy/DFA focused, optimal in predictable markets)
Shannon Entropy weight × 2.5, DFA weight × 2.5
Detects low-entropy predictable patterns and DFA persistence/mean-reversion signals
Failure mode: High-entropy chaos (hedged by Technical-Dominant)
Strategy 2: Structure-Dominant (Harmonic/BOS focused, optimal in clear swing structures)
Harmonics weight × 2.5, Liquidity (S/R) weight × 2.0
Uses swing detection, break-of-structure, and support/resistance clustering
Failure mode: Range-bound markets (hedged by Balanced)
Strategy 3: Technical-Dominant (RSI/MACD/Divergence focused, optimal in established trends)
RSI weight × 2.0, MACD weight × 2.0, Trend weight × 2.0
Zero-lag RSI suite with 4 calculation methods, MACD analysis, divergence detection
Failure mode: Choppy/ranging markets (hedged by chop filter)
Strategy 4: Balanced (Equal weighting, optimal in unknown/transitional regimes)
All components weighted 1.2×
Baseline performance during regime uncertainty
Strategy 5: Regime-Adaptive (Dynamic weighting by detected market state)
Chop zones: Kernel × 2.0, Technical × 0.3
Bull/Bear trends: Trend × 1.5, DFA × 2.0
Ranging: Mean reversion × 1.5
Adapts explicitly to detected regime
Multi-Armed Bandit System: 4 Core Algorithms
What Is a Multi-Armed Bandit Problem?
Formal Definition: K arms (strategies), each with unknown reward distribution. Goal: Maximize cumulative reward while learning which arms are best. Challenge: Balance exploration (trying uncertain strategies) vs. exploitation (using known-best strategy).
Trading Application: Each confluence strategy is an "arm." After each trade, receive reward (P&L percentage). Bandits decide which strategy to trust for next signal.
The 4 Implemented Algorithms
1. Thompson Sampling (DEFAULT)
Category: Bayesian approach with probability distributions
How It Works: Model each strategy as Beta(α, β) where α = wins, β = losses. Sample from distributions, select highest sample.
Properties: Optimal regret O(K log T), automatic exploration-exploitation balance
When To Use: Best all-around choice, adaptive markets, long-term optimization
2. UCB1 (Upper Confidence Bound)
Category: Frequentist approach with confidence intervals
Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i)
Properties: Deterministic, interpretable, same optimal regret as Thompson
When To Use: Prefer deterministic behavior, stable markets
3. Epsilon-Greedy
Category: Simple baseline with random exploration
How It Works: With probability ε (0.15): random strategy. Else: best average reward.
Properties: Simple, fast initial learning
When To Use: Baseline comparison, short-term testing
4. Contextual Bandit
Category: Context-aware Thompson Sampling
Enhancement: Maintains separate alpha/beta for Bull/Bear/Ranging regimes
Learning: "Strategy 2: 60% win rate in Bull, 40% in Bear"
When To Use: After 100+ bars, clear regime shifts
Shadow Portfolio System
Why Shadow Portfolios?
Traditional bandits use abstract scores. Shadow portfolios provide realistic performance measurement through actual position simulation.
How It Works
Position Opening:
When strategy generates validated signal:
Opens virtual position for selected strategy
Records: entry price, direction, entry bar, RSI method
Optional: Open positions for ALL strategies simultaneously (faster learning)
Position Management (Every Bar):
Current P&L: pnl_pct = (close - entry) / entry × direction × 100
Exit if: pnl_pct <= -2.0% (stop-loss) OR pnl_pct >= +4.0% (take-profit) OR held ≥ 100 bars (time)
Position Closing:
Calculate final P&L percentage
Update strategy equity, track win rate, gross profit/loss, max drawdown
Calculate risk-adjusted reward:
text
base_reward = pnl_pct / 10.0
win_rate_bonus = (win_rate - 0.5) × 0.3
drawdown_penalty = -max_drawdown × 0.05
total_reward = sigmoid(base + bonus + penalty)
Update bandit algorithms with reward
Update RSI method bandit
Statistics Tracked Per Strategy:
Equity curve (starts at $10,000)
Win rate percentage
Max drawdown
Gross profit/loss
Current open position
This creates closed-loop learning : Strategies compete → Best performers selected → Bandits learn quality → System adapts automatically.
Historical Pre-Training System
The Problem with Live-Only Learning
Standard bandits start with zero knowledge and need 50-100 signals to stabilize. For weekly timeframe traders, this could take years.
The Solution: Historical Training
During Chart Load: System processes last 300-1000 bars (configurable) in "training mode":
Detect signals using Balanced strategy (consistent baseline)
For each signal, open virtual training positions for all 5 strategies
Track positions through historical bars using same exit logic (SL/TP/time)
Update bandit algorithms with historical outcomes
CRITICAL TRANSPARENCY: Signal detection does NOT look ahead—signals use only data available at entry bar. Exit tracking DOES look ahead (uses future bars for SL/TP), which is acceptable because:
✅ Entry decisions remain valid (no forward bias)
✅ Learning phase only (not affecting shown signals)
✅ Real-time mirrors training (identical exit logic)
Training Completion: Once chart reaches current bar, system transitions to live mode. Dashboard displays training vs. live statistics for comparison.
Benefit: System begins live trading with 100-500 historical trades worth of learning, enabling immediate intelligent strategy selection.
Advanced Chop Zone Detection Engine
The Innovation: Multi-Layer Geometric Chop Analysis
Traditional chop filters use simple volatility metrics (ATR thresholds) that can't distinguish between trending volatility (good for signals) and choppy volatility (bad for signals). This system implements three-layer geometric pattern analysis to precisely identify consolidation zones where reversal signals fail.
Layer 1: Micro-Structure Chop Detection
Method: Analyzes micro pivot points (5-bar left, 2-bar right) to detect geometric compression patterns.
Slope Analysis:
Calculates slope of pivot high trendline and pivot low trendline
Compression ratio: compression = slope_high - slope_low
Pattern Classification:
Converging slopes (compression < -0.05) → "Rising Wedge" or "Falling Wedge"
Flat slopes (|slope| < 0.05) → "Rectangle"
Parallel slopes (|compression| < 0.1) → "Channel"
Expanding slopes → "Expanding Range"
Chop Scoring:
Rectangle pattern: +15 points (highest chop)
Low average slope (<0.05): +15 points
Wedge patterns: +12 points
Flat structures: +10 points
Why This Works: Geometric patterns reveal market indecision. Rectangles and wedges create false breakouts that trap technical traders. By quantifying geometric compression, system detects these zones before signals fire.
Layer 2: Macro-Structure Chop Detection
Method: Tracks major swing highs/lows using ATR-based deviation threshold (default 2.0× ATR), projects channel boundaries forward.
Channel Position Calculation:
proj_high = last_swing_high + (swing_high_slope × bars_since)
proj_low = last_swing_low + (swing_low_slope × bars_since)
channel_width = proj_high - proj_low
position = (close - proj_low) / channel_width
Dead Zone Detection:
Middle 50% of channel (position 0.25-0.75) = low-conviction zone
Score increases as price approaches center (0.5)
Chop Scoring:
Price in dead zone: +15 points (scaled by centrality)
Narrow channel width (<3× ATR): +15 points
Channel width 3-5× ATR: +10 points
Why This Works: Price in middle of range has equal probability of moving either direction. Institutional traders avoid mid-range entries. By detecting "dead zones," system avoids low-probability setups.
Layer 3: Volume Chop Scoring
Method: Low volume indicates weak conviction—precursor to ranging behavior.
Scoring:
Volume < 0.5× average: +20 points
Volume 0.5-0.8× average: +15 points
Volume 0.8-1.0× average: +10 points
Overall Chop Intensity & Signal Filtering
Total Chop Calculation:
chop_intensity = micro_score + macro_score + (volume_score × volume_weight)
is_chop = chop_intensity >= 40
Signal Filtering (Three-Tier Approach):
1. Signal Blocking (Intensity > 70):
Extreme chop detected (e.g., tight rectangle + dead zone + low volume)
ALL signals blocked regardless of confluence
Chart displays red/orange background shading
2. Threshold Adjustment (Intensity 40-70):
Moderate chop detected
Confluence threshold increased: threshold += (chop_intensity / 50)
Only highest-quality signals pass
3. Strategy Weight Adjustment:
During Chop: Kernel-Dominant weight × 2.0 (entropy detects breakout precursors), Technical-Dominant weight × 0.3 (reduces false signals)
After Chop Exit: Weights revert to normal
Why This Three-Tier Approach Is Original: Most chop filters simply block all signals (loses breakout entries). This system adapts strategy selection during chop—allowing Kernel-Dominant (which excels at detecting low-entropy breakout precursors) to operate while suppressing Technical-Dominant (which generates false signals in consolidation). Result: System remains functional across full market regime spectrum.
Zero-Lag Filter Suite with Dynamic Volatility Scaling
Zero-Lag ADX (Trend Regime Detection)
Implementation: Applies ZLEMA to ADX components:
lag = (length - 1) / 2
zl_source = source + (source - source ) × strength
Dynamic Volatility Scaling (DVS):
Calculates volatility ratio: current_ATR / ATR_100period_avg
Adjusts ADX length dynamically: High vol → shorter length (faster), Low vol → longer length (smoother)
Regime Classification:
ADX > 25 with +DI > -DI = Bull Trend
ADX > 25 with -DI > +DI = Bear Trend
ADX < 25 = Ranging
Zero-Lag RSI Suite (4 Methods with Bandit Selection)
Method 1: Standard RSI - Traditional Wilder's RSI
Method 2: Ehlers Zero-Lag RSI
ema1 = ema(close, length)
ema2 = ema(ema1, length)
zl_close = close + (ema1 - ema2)
Method 3: ZLEMA RSI
lag = (length - 1) / 2
zl_close = close + (close - close )
Method 4: Kalman-Filtered RSI - Adaptive smoothing with process/measurement noise
RSI Method Bandit: Separate 4-arm bandit learns which calculation method produces best results. Updates independently after each trade.
Kalman Adaptive Filters
Fast Kalman: Low process noise → Responsive to genuine moves
Slow Kalman: Higher measurement noise → Filters noise
Application: Crossover logic for trend detection, acceleration analysis for momentum inflection
What Makes This Original
Innovation 1: Shadow Portfolio Validation
First TradingView script to implement parallel virtual portfolios for multi-armed bandit reward calculation. Instead of abstract scoring metrics, each strategy's performance is measured through realistic position tracking with stop-loss, take-profit, time-based exits, and risk-adjusted reward functions (P&L + win rate + drawdown). This provides orders-of-magnitude better reward signal quality for bandit learning than traditional score-based approaches.
Innovation 2: Three-Layer Geometric Chop Detection
Novel multi-scale geometric pattern analysis combining: (1) Micro-structure slope analysis with pattern classification (wedges, rectangles, channels), (2) Macro-structure channel projection with dead zone detection, (3) Volume confirmation. Unlike simple volatility filters, this system adapts strategy weights during chop —boosting Kernel-Dominant (breakout detection) while suppressing Technical-Dominant (false signal reduction)—allowing operation across full market regime spectrum without blind signal blocking.
Innovation 3: Historical Pre-Training System
Implements two-phase learning : Training phase (processes 300-1000 historical bars on chart load with proper state isolation) followed by live phase (real-time learning). Training positions tracked separately from live positions. System begins live trading with 100-500 trades worth of learned experience. Dashboard displays training vs. live performance for transparency.
Innovation 4: Contextual Multi-Armed Bandits with Regime-Specific Learning
Beyond standard bandits (global strategy quality), implements regime-specific alpha/beta parameters for Bull/Bear/Ranging contexts. System learns: "Strategy 2: 60% win rate in ranging markets, 45% in bull trends." Uses current regime's learned parameters for strategy selection, enabling regime-aware optimization.
Innovation 5: RSI Method Meta-Learning
Deploys 4 different RSI calculation methods (Standard, Ehlers ZL, ZLEMA, Kalman) with separate 4-arm bandit that learns which calculation works best. Updates RSI method bandit independently based on trade outcomes, allowing automatic adaptation to instrument characteristics.
Innovation 6: Dynamic Volatility Scaling (DVS)
Adjusts ALL lookback periods based on current ATR ratio vs. 100-period average. High volatility → shorter lengths (faster response). Low volatility → longer lengths (smoother signals). Applied system-wide to entropy, DFA, RSI, ADX, and Kalman filters for adaptive responsiveness.
How to Use: Practical Guide
Initial Setup (5 Minutes)
Theory Mode: Start with "BALANCED" (APEX for aggressive, CONSERVATIVE for defensive)
Enable RL: Toggle "Enable RL Auto-Optimization" to TRUE, select "Thompson Sampling"
Enable Confluence Modules: Divergence, Volume Analysis, Liquidity Mapping, RSI OB/OS, Trend Analysis, MACD (all recommended)
Enable Chop Filter: Toggle "Enable Chop Filter" to TRUE, sensitivity 1.0 (default)
Historical Training: Enable "Enable Historical Pre-Training", set 300-500 bars
Dashboard: Enable "Show Dashboard", position Top Right, size Large
Learning Phase (First 50-100 Bars)
Monitor Thompson Sampling Section:
Alpha/beta values should diverge from initial 1.0 after 20-30 trades
Expected win% should stabilize around 55-60% (excellent), >50% (acceptable)
"Pulls" column should show balanced exploration (not 100% one strategy)
Monitor Shadow Portfolios:
Equity curves should diverge (different strategies performing differently)
Win rate > 55% is strong
Max drawdown < 15% is healthy
Monitor Training vs Live (if enabled):
Delta difference < 10% indicates good generalization
Large negative delta suggests overfitting
Large positive delta suggests system adapting well
Optimization:
Too few signals: Lower "Base Confluence Threshold" to 2.5-3.0
Too many signals: Raise threshold to 4.0-4.5
One strategy dominates (>80%): Increase "Exploration Rate" to 0.20-0.25
Excessive chop blocking: Lower "Chop Sensitivity" to 0.7-0.8
Signal Interpretation
Dashboard Indicators:
"WAITING FOR SIGNAL": No confluence
"LONG ACTIVE ": Validated long entry
"SHORT ACTIVE ": Validated short entry
Chart Visuals:
Triangle markers: Entry signal (green = long, red = short)
Orange/red background: Chop zone
Lines: Support/resistance if enabled
Position Management
Entry: Enter on triangle marker, confirm direction matches dashboard, check confidence >60%
Stop-Loss: Entry ± 1.5× ATR or at structural swing point
Take-Profit:
TP1: Entry + 1.5R (take 50%, move SL to breakeven)
TP2: Entry + 3.0R (runner) or trail
Position Sizing:
Risk per trade = 1-2% of capital
Position size = (Account × Risk%) / (Entry - SL)
Recommended Settings by Instrument
Stocks (Large Cap): Balanced mode, Threshold 3.5, Thompson Sampling, Chop 1.0, 15min-1H, Training 300-500 bars
Forex Majors: Conservative-Balanced mode, Threshold 3.5-4.0, Thompson Sampling, Chop 0.8-1.0, 5min-30min, Training 400-600 bars
Cryptocurrency: Balanced-APEX mode, Threshold 3.0-3.5, Thompson Sampling, Chop 1.2-1.5, 15min-4H, Training 300-500 bars
Futures: Balanced mode, Threshold 3.5, UCB1 or Thompson, Chop 1.0, 5min-30min, Training 400-600 bars
Technical Approximations & Limitations
1. Thompson Sampling: Pseudo-Random Beta Distribution
Standard: Cryptographic RNG with true beta sampling
This Implementation: Box-Muller transform using market data as entropy source
Impact: Not cryptographically random but maintains exploration-exploitation balance. Sufficient for strategy selection.
2. Shadow Portfolio: Simplified Execution Model
Standard: Order book simulation with slippage, partial fills
This Implementation: Perfect fills at close price, no fees modeled
Impact: Real-world performance ~0.1-0.3% worse per trade due to execution costs.
3. Historical Training: Forward-Looking for Exits Only
Entry signals: Use only past data (causal, no bias)
Exit tracking: Uses future bars to determine SL/TP (forward-looking)
Impact: Acceptable because: (1) Entry logic remains valid, (2) Live trading mirrors training, (3) Improves learning quality. Training win rates reflect 8-bar evaluation window—live performance may differ if positions held longer.
4. Shannon Entropy & DFA: Simplified Calculations
Impact: 10-15% precision loss vs. academic implementations. Still captures predictability and persistence signals effectively.
General Limitations
No Predictive Guarantee: Past performance ≠ future results
Learning Period Required: Minimum 50-100 bars for stable statistics
Overfitting Risk: May not generalize to unprecedented conditions
Single-Instrument: No multi-asset correlation or sector context
Execution Assumptions: Degrades in illiquid markets (<100k volume), major news events, flash crashes
Risk Warnings & Disclaimers
No Guarantee of Profit: All trading involves substantial risk of loss. This indicator is a tool, not a guaranteed profit system.
System Failures: Software bugs possible despite testing. Use appropriate position sizing.
Market Regime Changes: Performance may degrade during extreme volatility (VIX >40), low liquidity periods, or fundamental regime shifts.
Broker-Specific Issues: Real-world execution includes slippage (0.1-0.5%), commissions, overnight financing costs, partial fills.
Forward-Looking Bias in Training: Historical training uses 8-bar forward window for exit evaluation. Dashboard "Training Win%" reflects this method. Real-time performance may differ.
Appropriate Use
This Indicator IS:
✅ Entry trigger system with confluence validation
✅ Risk management framework (automated SL/TP)
✅ Adaptive strategy selection engine
✅ Learning system that improves over time
This Indicator IS NOT:
❌ Complete trading strategy (requires position sizing, portfolio management)
❌ Replacement for due diligence
❌ Guaranteed profit generator
❌ Suitable for complete beginners
Recommended Complementary Analysis: Market context, volume profile, fundamental catalysts, higher timeframe alignment, support/resistance from other sources.
Conclusion
Chronos Reversal Labs V2.0 - Elite Edition synthesizes research from multi-armed bandit theory (Thompson Sampling, UCB, contextual bandits), market microstructure (geometric chop detection, zero-lag filters), and machine learning (shadow portfolio validation, historical pre-training, RSI method meta-learning).
Unlike typical indicator mashups, this system implements mathematically rigorous bandit algorithms with realistic performance validation, three-layer chop detection with adaptive strategy weighting, regime-specific learning, and full transparency on approximations and limitations.
The system is designed for intermediate to advanced traders who understand that no indicator is perfect, but through proper machine learning and realistic validation, we can build systems that improve over time and adapt to changing markets without manual intervention.
Use responsibly. Understand the limitations. Risk disclosure applies. Past performance does not guarantee future results.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Z-Score IndicatorA Z-Score measures how many standard deviations a value is from its mean.
In finance, it indicates how far the current price is from its historical average in statistical terms.
Practically speaking, the Z-Score quantifies price anomalies and serves as the statistical foundation behind mean-reversion strategies and dispersion analysis (pairs trading, Z-bands, etc.).
±1σ: normal movement.
±2σ: moderate overextension.
±3σ: statistically extreme event (≈ 0.3% probability under a normal distribution).
Static Beta for Pair and Quant Trading A beta coefficient shows the volatility of an individual stock compared to the systematic risk of the entire market. Beta represents the slope of the line through a regression of data points. In finance, each point represents an individual stock's returns against the market.
Beta effectively describes the activity of a security's returns as it responds to swings in the market. It is used in the capital asset pricing model (CAPM), which describes the relationship between systematic risk and expected return for assets. CAPM is used to price risky securities and to estimate the expected returns of assets, considering the risk of those assets and the cost of capital.
Calculating Beta
A security's beta is calculated by dividing the product of the covariance of the security's returns and the market's returns by the variance of the market's returns over a specified period. The calculation helps investors understand whether a stock moves in the same direction as the rest of the market. It also provides insights into how volatile—or how risky—a stock is relative to the rest of the market.
For beta to provide useful insight, the market used as a benchmark should be related to the stock. For example, a bond ETF's beta with the S&P 500 as the benchmark would not be helpful to an investor because bonds and stocks are too dissimilar.
Beta Values
Beta equal to 1: A stock with a beta of 1.0 means its price activity correlates with the market. Adding a stock to a portfolio with a beta of 1.0 doesn’t add any risk to the portfolio, but it doesn’t increase the likelihood that the portfolio will provide an excess return.
Beta less than 1: A beta value less than 1.0 means the security is less volatile than the market. Including this stock in a portfolio makes it less risky than the same portfolio without the stock. Utility stocks often have low betas because they move more slowly than market averages.
Beta greater than 1: A beta greater than 1.0 indicates that the security's price is theoretically more volatile than the market. If a stock's beta is 1.2, it is assumed to be 20% more volatile than the market. Technology stocks tend to have higher betas than the market benchmark. Adding the stock to a portfolio will increase the portfolio’s risk, but may also increase its return.
Negative beta: A beta of -1.0 means that the stock is inversely correlated to the market benchmark on a 1:1 basis. Put options and inverse ETFs are designed to have negative betas. There are also a few industry groups, like gold miners, where a negative beta is common.
LET'S START
Now I'll give my own definition.
Beta:
If we assume market caps are equal ,
it is an indicator that shows how much of the second instrument we should buy if we buy one of the first, taking into account the price volatility of two instruments.
But if the market caps are not equal:
For example, the ETF for A is $300.
The ETF for B is $600.
If static beta predicted by this script is 0.5:
300 * 1 * a = 600 * 0.5 * b
Then we should use 1 b for 1 a.
(Long a and short b or vice versa )
So, we can try pair trading for a/b or a-b.
However, these values are generally close to each other, such as 0.8 and 0.93. However, the closer we can adjust our lot purchases to bring the double beta to a value closer to 1, the higher the hedge ratio will be.
Large commercials use dynamic betas, which are updated periodically, in addition to static betas
However, scaling this is very difficult for individual investors with limited investment tools.
But a static beta of 5,000 bars is still much better than not considering any beta at all.
Note: The presence of a beta value for two instruments does not necessarily mean they can be included in pair trading.
It is also important (%99) to consider historically very high correlations and cointegration relationships, as well as the compatibility of security structures.
Note 2 : This script is designed for low timeframes.
Do not use betas from different timeframes.
Beta dynamics are different for each timeframe.
Note 3 : I created this script with the help of ChatGPT.
Source for beta definition ( ) :
www.investopedia.com
Regards.
Breakouts & Pullbacks [Trendoscope®]🎲 Breakouts & Pullbacks - All-Time High Breakout Analyzer
Probability-Based Post-Breakout Behavior Statistics | Real-Time Pullback & Runup Tracker
A professional-grade Pine Script v6 indicator designed specifically for analyzing the historical and real-time behavior of price after strong All-Time High (ATH) breakouts. It automatically detects significant ATH breakouts (with configurable minimum gap), measures the depth and duration of pullbacks, the speed of recovery, and the subsequent run-up strength — then turns all this data into easy-to-read statistical probabilities and percentile ranks.
Perfect for swing traders, breakout traders, and anyone who wants objective, data-driven insight into questions like:
“How deep do pullbacks usually get after a strong ATH breakout?”
“How many bars does it typically take to recover the breakout level?”
“What is the median run-up after recovery?”
“Where is the current pullback or run-up relative to historical ones?”
🎲 Core Concept & Methodology
Indicator is more suitable for indices or index ETFs that generally trade in all-time highs however subjected to regular pullbacks, recovery and runups.
For every qualified ATH breakout, the script identifies 4 distinct phases:
Breakout Point – The exact bar where price closes above the previous ATH after at least Minimum Gap bars.
Pullback Phase – From breakout candle high → lowest low before price recovers back above the breakout level.
Recovery Phase – From the pullback low → the bar where price first trades back above the original breakout price.
Post-Recovery Run-up Phase – From the recovery point → current price (or highest high achieved so far).
Each completed cycle is stored permanently and used to build a growing statistical database unique to the loaded chart and timeframe.
🎲 Visual Elements
Yellow polyline triangle connecting Previous ATH / Pullback point(start), New ATH Breakout point (end), Recovery point (lowest pullback price), and extends to recent ATH price.
Small green label at the pullback low showing detailed tooltip on hover with all measured values
Clean, color-coded statistics table in the top-right corner (visible only on the last bar)
Powerful Statistics Table – The Heart of the Indicator
The table constantly compares the current situation against all past qualified breakouts and shows details about pullbacks, and runups that help us calculate the probability of next pullback, recovery or runup.
🎲 Settings & Inputs
Minimum Gap
The minimum number of bars that must pass between breaking a new ATH and the previous one.
Higher values = stricter filter → only the strongest, cleanest breakouts are counted.
Lower values = more data points (useful on lower timeframes or very trending instruments).
Recommendation:
Daily charts: 30–50
4H charts: 40–80
1H charts: 100–200
🎲 How to Use It in Practice
This indicator helps investors to understand when to be bullish, bearish or cautious and anticipate regular pullbacks, recovery of markets using quantitative methods.
The indicator does not generate buy/sell signals. However, helps traders set expectations and anticipate market movements based on past behavior.
Options Premium Decay (Paisa Algo)📜 Option Premium Analysis (Paisa Algo): Key Concepts
Option Premium Analysis is the process of evaluating the price (premium) of an options contract that a trader pays in advance to enter the contract.
Analyzing the premium is crucial as it significantly affects the potential returns on the contracts and helps in deciding the appropriate trading strategy.
Factors Affecting Premium Price
The option premium is influenced by several factors:
Intrinsic Value: The difference between the underlying asset's current market price and the strike price. It is always positive or zero, never negative.
Time Value (Extrinsic Value): Represents the potential for the contract's value to change before expiry. This value decays as the expiry date approaches, a phenomenon known as
Option Premium Time Decay Analysis.
Volatility: Higher volatility in the stock price leads to higher premiums.
Rate of Interest: A higher rate of interest suggests higher premiums.
Dividends: The payment of dividends can significantly impact option pricing, especially for call options, as the holder is not entitled to the dividend
Underlying Asset Price: Changes in the underlying asset's price can impact the options premium.
Calculation Methods
Two popular methods for calculating the options premium and its decay are the Black-Scholes model and the Binomial model .
📊 "Options Premium Decay (Paisa Algo)" Indicator
This is a technical indicator written in Pine Script designed to visualize and alert on the decay or change in premium of a selected range of Call (CE) and Put (PE) options for a given underlying asset (like NIFTY).
Key Functionality
Focus: It performs Option Premium Decay Analysis by measuring the rate of decline in the value of an options contract due to the passage of time.
Input Parameters:
Symbol: The underlying asset (e.g., `NSE:NIFTY`).
Expiry Dt: The expiration date for the options contracts.
Strike Range: Defined by `Strike` (lower), `Strike` (upper), and `Strike Diff`.
Calculation:
It auto-generates option tickers for the specified strike range and expiry date.
It requests the closing price (`close`) for each Call (CE) and Put (PE) option contract within the range.
It calculates the change since the open for the total premium of all fetched CE contracts (`ce_decay`) and all fetched PE contracts (`pe_decay`).
Output Visualization:
It plots the CE Decay (green/teal) and PE Decay (r ed) lines, showing the change in the total premium since the start of the session.
It displays percentage badges on the right edge of the chart to show the relative contribution of CE and PE decay to the total absolute decay sum.
It includes a `0` line for reference.
Alerts and Markers: The indicator generates alerts and places on-chart markers for specific conditions:
Decay Cross: When the CE and PE decay lines cross.
Both At Zero: When both CE and PE decay values are near zero.
Both Below Zero: When both CE and PE decay values are negative
TVB - Thomas Volatility Bands v2.0TVB – Thomas Volatility Bands v2.0
Author: Thomas Aaroon
Concept: CIV-Driven Volatility Bands with Adaptive Vomma Scaling
Overview
TVB – Thomas Volatility Bands v2.0 is an advanced volatility-adaptive band system built on two core elements:
CIV (Composite Implied Volatility) – manually provided or proxied using an external IV index
Dynamic Vomma Scaling – a higher-order volatility response factor that adjusts band width based on the convexity of implied volatility changes
Together, these components create a continuously adapting volatility envelope that reacts smoothly to market regime shifts.
Key Features
1. Flexible CIV Input
Manual CIV mode: Enter your own CIV value (decimal or %)
Proxy CIV mode: Pulls IV data from INDIA_VIX or any custom IV symbol
Weighted blending: Adjustable α-weight for proxy influence
Automatic normalization ensures stable and bounded CIV values.
2. Adaptive Volatility Engine
CIV is smoothed using EMA for intraday and SMA for higher-timeframes
Vomma coefficient dynamically adjusts based on CIV percentile and short-term CIV volatility
Produces a volatility surface that expands during stress and contracts during calm periods.
3. Time-Scaled Band Construction
Bands automatically scale their width according to:
Timeframe multiplier
Estimated bars-per-day
Annualized volatility normalization (√252 rule)
This ensures consistent volatility geometry across all chart timeframes.
4. Dual-Layer Volatility Bands
Inner Bands (±3σ): Tactical mean-reversion boundaries
Outer Bands (±4σ): Structural deviation zones for extreme price dislocations
Smooth color-coded volatility regimes (low/moderate/high CIV).
5. Re-Entry Logic (34% Rule)
A clean, rule-based mechanism inspired by distributional penetration depth:
Tracks bars that break the ±4σ outer band
Looks for 34% penetration back toward the ±3σ region
Generates optional visual markers (buy/sell re-entry)
Designed to highlight volatility compression opportunities after extreme expansions.
6. Optional CIV Diagnostic Label
Shows:
CIV and smooth CIV
Vomma coefficient
Effective band width
Useful for strategy development and volatility research.
Intended Use
TVB v2.0 is designed for:
Volatility-based trading models
Mean-reversion and re-entry systems
Volatility regime identification
Institutional-grade market structure research
This indicator does not repaint and does not generate trade signals by default (signals can be enabled via optional shapes).
Disclaimer
This tool is for educational and analytical purposes only.
It is not financial advice, and the author is not responsible for any trading outcomes.
OSOK - One Shot One Kill( Macros w/ Body Swings, SD Prj)What you get:
Time windows: contiguous 50→10 (HH:50–(HH+1):10) and 20→40 (HH:20–HH:40), or both.
Kill Zones & Day filter: Asian, London, NY, London Close; weekdays toggles.
Static projection TF: compute swings on 5-minute (or custom) and display on any chart TF.
Fibonacci/SD ladder: internal retracements & multi-SD extensions with optional price labels.
Stats table: per-hour counts, average/ min/ max range, plus hit-rates for +1/+2/+3/+4 and −1/−2.
Sequence logic (optional): track conditional paths (e.g., 0→+2, +1→−2, etc.) to separate continuation vs. reversal behavior.
CSV export: push current table (filtered/sorted) to a chart label for copy-out.
Multi-Asset % Performance Table | v2.1 | TCP Multi-Asset % Performance Table | v2.1 | TCP
ESSENTIAL SUMMARY:
Multi-Asset % Performance Table eliminates the need to manually draw and manage individual "Price Range" tools for every asset. It automatically tracks up to 15 tickers independently in a single dashboard, calculating a TOTAL SCORE (Portfolio Average) for you. Unlike manual drawings, it supports a Global Range while allowing Custom Dates for specific assets, ensuring each ticker is calculated based on its own precise entry/exit. The Smart Visuals dynamically draw the correct date lines only for the ticker you are currently viewing, keeping your chart automatic, accurate, and clutter-free.
FUL DESCRIPTION:
📊 What is this tool?
The Multi-Asset % Performance Table is a powerful portfolio dashboard designed to track the percentage performance of up to 15 different assets simultaneously.
Instead of checking tickers one by one or manually drawing price ranges, this indicator aggregates everything into a single, clean table. It allows you to compare the ROI (Return on Investment) of a basket of coins or stocks over a specific time period and calculates an aggregate TOTAL SCORE (Average %) for your selection.
🚀 Key Features
15 Asset Slots: Monitor up to 15 different tickers (Crypto, Stocks, Forex, etc.) in one view.
Global vs. Custom Dates: Set a "Global" start/end date for the whole portfolio, but override specific assets with Custom Dates if they entered the portfolio at a different time.
Smart Visuals: Automatically draws vertical dashed lines on your chart representing the start and end dates of the ticker you are currently viewing.
Total Score Calculation: Calculates the average percentage change of your portfolio. You can dynamically include or exclude specific assets from this average using the settings.
Status Column: A quick visual reference (✔ or ✘) in the table showing which assets are currently included in the Total Score calculation.
⚙️ How it Works
Data Fetching: The script pulls "Close" prices from the Daily timeframe to ensure accuracy across long periods.
Smart Matching: The visual lines automatically detect which asset you are viewing. For example, if you are looking at BTCUSDT and have custom dates set for it, the vertical lines will jump to those specific dates. If you view a ticker not in your list, it defaults to the Global dates.
Visual Protection: The script uses advanced logic to ensure only one set of range lines appears on the chart at a time, keeping your workspace clean.
🛠️ Instructions & Settings
1. Setting up your Assets
Open the Settings (Cogwheel icon).
Under ASSET 1 through ASSET 15, enter the tickers you want to track (e.g., BINANCE:BTCUSDT).
Include in Avg?: Uncheck this if you want to see the asset in the table but exclude it from the "TOTAL SCORE" average.
2. Defining Time Ranges
Global Settings: Set the Global Start and Global End dates at the top. This applies to all assets by default.
Custom Dates: If a specific asset (e.g., Asset 4) was bought on a different day, check the "Custom Dates?" box for that asset and enter its specific Start/End time.
3. Reading the Table
The table appears on the chart (default: Bottom Right) with three columns:
Asset: The name of the ticker.
% Change: The percentage move from Start Date to End Date. (Green = Positive, Red = Negative).
Inc: Shows a ✔ if the asset is included in the Total Score average, or a ✘ if excluded.
4. The Visual Lines
Two vertical dashed lines will appear on your chart.
Note: These lines are visual references only. You cannot drag them to change the dates. To change the dates, you must use the Settings menu.
💡 Tips
Hover for Details: Hover your mouse over the % Change value in the table to see a tooltip showing the exact Start Price and End Price used for the calculation.
Resolution: The script defaults to 1 Day resolution for optimal accuracy on historical data.
v2.1 | TCP - Custom Built for Precision Performance Tracking
Last CLOSED Bar OHLCThis TradingView Pine Script (@version=6) creates a label that displays the previous fully closed candlestick’s OHLC data on the chart.
Quant Master Flow [Cumulative Volume Delta]Quant Master Flow
The Quant Master Flow indicator is a tool that analyzes market aggression by tracking the Cumulative Volume Delta (CVD), providing critical insight into institutional participation and short-term liquidity absorption. It acts as the "Conviction Filter" to confirm the statistical signals provided by the Z-Oscillator.
Core Philosophy: Aggression vs. Absorption
The CVD measures the running total of the difference between aggressive buyer-initiated volume and aggressive seller-initiated volume. By plotting this cumulative total, the indicator reveals whether the net effect of market orders is one of accumulation (aggressive buying, driving the price up) or distribution (aggressive selling, driving the price down).
Key Components
Cumulative Tally: The indicator plots the running sum of the volume delta. A rising CVD suggests buyers are more aggressive than sellers; a falling CVD suggests the reverse.
Color Coding: The CVD is colored to visualize flow:
Green: Periods of net aggressive buying (accumulation).
Red: Periods of net aggressive selling (distribution).
Volume Thresholds (Optional/Implied): Allows for filtering of low-impact noise, ensuring the cumulative line only reflects significant shifts in order flow.
Strategic Use Cases
The power of the Quant Master Flow is realized by comparing its trajectory to the price action, validating Z-Score extremes, and spotting liquidity grabs.
1. High-Conviction Confirmation
Use the CVD to confirm a directional signal from the Z-Oscillator:
Bullish Confirmation: When the Z-Oscillator hits Oversold ($\pm 2\sigma$) and the price begins to move up, a strong rising (Green) CVD confirms that the reversal is being fueled by institutional accumulation.
Bearish Confirmation: When the Z-Oscillator hits Overbought ($\pm 2\sigma$) and the price begins to fall, a strong falling (Red) CVD confirms that the drop is being driven by institutional distribution.
2. Divergence (The Early Warning System)
Divergence between the CVD and price is the strongest signal of impending failure or reversal, indicating that the current price movement is unsupported by institutional commitment.
Bearish Divergence: Price makes a Higher High while the CVD makes a Lower High. This is a warning that institutional players are distributing into the rally, signaling a failure to continue the trend.
Bullish Divergence: Price makes a Lower Low while the CVD makes a Higher Low. This shows institutional accumulation is occurring despite falling prices, often preceding a strong reversal.
3. Flow Exhaustion
When the CVD line flattens out during a strong price rally or drop, it signals that the market aggression is exhausted. This often happens right before the Z-Oscillator hits its $\pm 3\sigma$ Extreme zone, providing the earliest warning of a statistical reversal.
Quant Master Z-Oscillator [Risk + Bias]his indicator is a statistically-driven oscillator designed to measure the extreme deviation of price from its recent mean, identifying both reversal risk and directional bias within the current trend. It reframes classic Z-Score analysis to provide a quantified framework for trade timing and risk assessment.
Core Philosophy
The primary goal is to determine the statistical probability of a mean-reversion event. By measuring how many standard deviations the current price is away from its simple moving average (the basis), the indicator identifies moments of maximum risk (Extremes) and optimal entry (Oversold/Overbought zones).
Key Components
Z-Score Calculation:
Measures the distance of the closing price from the Lookback Length Simple Moving Average (SMA), normalized by the Standard Deviation (Volatility).
The raw score is then smoothed using an Exponential Moving Average (EMA) to filter noise, providing a clearer reading of the underlying statistical position.
Statistical Thresholds:
$\pm 2\sigma$ (High/Low): Defines the standard Overbought/Oversold zones (Trigger Zones). Movement into these areas suggests a pullback or reversal is increasingly likely.
$\pm 3\sigma$ (Extreme): Defines the "Kill Zone" of maximum statistical risk. Price reaching this level is highly unlikely to sustain itself, triggering an Extreme Overbought/Oversold warning.
Risk & Bias Dashboard (Table):
A real-time dashboard displayed on the chart (bottom right) provides a quantified summary of the current market state:
Current Z: The exact Z-Score value and its gradient color (green for positive pressure, red for negative).
Market Risk: Flags the statistical risk (e.g., OVERBOUGHT or EXTREME OVERSOLD ⚠️) based on the $\sigma$ thresholds.
Next Bias: Suggests the immediate directional bias (e.g., LONG SETUP NEXT or SHORT REVERSAL), helping the user prepare for the next high-probability setup based on the Z-Score's position relative to the mean.
Divergence Engine:
Detects standard Bullish and Bearish divergences between the Z-Score and the price action, signaling potential trend exhaustion or hidden momentum shifts.
Interpretation & Use
Risk Management: Treat the $\pm 3\sigma$ (Extreme) levels as mandatory profit-taking or high-alert reversal zones. Trading against these extremes carries the highest statistical risk.
Entry Timing: High-probability entries are found when the Z-Score is at $\pm 2\sigma$ (Oversold/Overbought) and a momentum shift (e.g., a green bar after an Oversold red sequence) is observed.
Trend Confirmation: When the Z-Score operates between $0$ and $\pm 2\sigma$, it confirms the direction of the current trend (Positive Z-Score = Bullish bias).
Bitcoin vs M2 Global Liquidity (Lead 3M) - Table Ticker═══════════════════════════════════════════════════════════════
Bitcoin vs M2 Global Liquidity - Regression Indicator
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TECHNICAL SPECS
• Pine Script v6
• Overlay: false (separate pane)
• Data sources: 5 M2 series + 4 FX pairs (request.security)
• Calculation: Rolling OLS linear regression with configurable lead
• Output: Regression line + ±1σ/±2σ confidence bands + R² ticker
CORE FUNCTIONALITY
Aggregates M2 money supply from 5 central banks (CN, US, EU, JP, GB),
converts to USD, applies time-lead, runs rolling linear regression
vs Bitcoin price, plots predicted value with confidence intervals.
CONFIGURABLE PARAMETERS
Input Controls:
• Lead Period: 0-365 days (default: 90)
• Lookback Window: 50-2000 bars (default: 750)
• Bands: Toggle ±1σ and ±2σ visibility
• Colors: BTC, M2, regression line, confidence zones
• Ticker: Position, size, colors, transparency
Advanced Settings:
• Table display: R², lead, M2 total, country breakdown (%)
• Ticker customization: 9 position options, 6 text sizes
• Border: Width 0-10px, color, outline-only mode
DATA AGGREGATION
Sources (via request.security):
• ECONOMICS:CNM2, USM2, EUM2, JPM2, GBM2
• FX_IDC:CNYUSD, JPYUSD (others: FX:EURUSD, GBPUSD)
• Conversion: All M2 → USD → Sum / 1e12 (trillions)
REGRESSION ENGINE
• Arrays: m2Array, btcArray (dynamic sizing, auto-trim)
• Window: Rolling (lookbackPeriod bars)
• Lead: Time-shift via array indexing (i + leadPeriodDays)
• Calc: Manual OLS (covariance/variance), no built-in ta functions
• Outputs: slope, intercept, r2, stdResiduals
CONFIDENCE BANDS
±1σ and ±2σ calculated from standard deviation of residuals.
Fill zones between upper/lower bounds with configurable transparency.
ALERTS
5 pre-configured alertcondition():
• Divergence > 15%
• Price crosses ±1σ bands (up/down)
• Price crosses ±2σ bands (up/down)
TICKER TABLE
Dynamic table.new() with 9 rows:
• R² value (4 decimals)
• Lead period (days + months)
• M2 Global total (trillions USD)
• Country breakdown: CN, US, EU, JP, GB (absolute + %)
• Optional: Hide/show M2 details
VISUAL CUSTOMIZATION
All plot() elements support:
• Color picker inputs (group="Couleurs")
• Line width: 1-3px
• Transparency: 0-100% for zones
• Offset: M2 plot has +leadPeriodDays offset option
PERFORMANCE
• Max arrays size: lookbackPeriod + leadPeriodDays + 200
• Calculations: Only when array.size >= lookbackPeriod + leadPeriodDays
• Table update: barstate.islast (once per bar)
• Request.security: gaps_off mode
CODE STRUCTURE
1. Inputs (lines 7-54)
2. Data fetch (lines 56-76)
3. M2 aggregation (line 78)
4. Array management (lines 84-95)
5. Regression calc (lines 97-172)
6. Prediction + bands (lines 174-183)
7. Plots (lines 185-199)
8. Ticker table (lines 201-236)
9. Alerts (lines 238-246)
DEPENDENCIES
None. Pure Pine Script v6. No external libraries.
LIMITATIONS
• Daily timeframe recommended (1D)
• Requires 750+ bars history for optimal calculation
• M2 data availability: TradingView ECONOMICS feed
• Max lines: 500 (declared in indicator())
CUSTOMIZATION EXAMPLES
• Shorter lookback (200d): More reactive, lower R²
• Longer lookback (1500d): More stable, regime mixing
• No bands: Set showBands=false for clean view
• Different lead: Test 60d, 120d for sensitivity analysis
TECHNICAL NOTES
• Manual OLS implementation (no ta.linreg)
• Array-based lead application (not plot offset)
• M2 values stored in trillions (/ 1e12) for readability
• Residuals array cleared/rebuilt each calculation
OPEN SOURCE
Code fully visible. Modify, fork, analyze freely.
No hidden calculations. No proprietary data.
VERSION
1.0 | November 2025 | Pine Script v6
═══════════════════════════════════════════════════════════════
High Volume Bars (Advanced)High Volume Bars (Advanced)
High Volume Bars (Advanced) is a Pine Script v6 indicator for TradingView that highlights bars with unusually high volume, with several ways to define “unusual”:
Classic: volume > moving average + N × standard deviation
Change-based: large change in volume vs previous bar
Z-score: statistically extreme volume values
Robust mode (optional): median + MAD, less sensitive to outliers
It can:
Recolor candles when volume is high
Optionally highlight the background
Optionally plot volume bands (center ± spread × multiplier)
⸻
1. How it works
At each bar the script:
Picks the volume source:
If Use Volume Change vs Previous Bar? is off → uses raw volume
If on → uses abs(volume - volume )
Computes baseline statistics over the chosen source:
Lookback bars
Moving average (SMA or EMA)
Standard deviation
Optionally replaces mean/std with robust stats:
Center = median (50th percentile)
Spread = MAD (median absolute deviation, scaled to approx σ)
Builds bands:
upper = center + spread * multiplier
lower = max(center - spread * multiplier, 0)
Flags a bar as “high volume” if:
It passes the mode logic:
Classic abs: volume > upper
Change mode: abs(volume - volume ) > upper
Z-score mode: z-score ≥ multiplier
AND the relative filter (optional): volume > average_volume * Min Volume vs Avg
AND it is past the first Skip First N Bars from the start of the chart
Colors the bar and (optionally) the background accordingly.
⸻
2. Inputs
2.1. Statistics
Lookback (len)
Number of bars used to compute the baseline stats (mean / median, std / MAD).
Typical values: 50–200.
StdDev / Z-Score Multiplier (mult)
How far from the baseline a bar must be to count as “high volume”.
In classic mode: volume > mean + mult × std
In z-score mode: z ≥ mult
Typical values: 1.0–2.5.
Use EMA Instead of SMA? (smooth_with_ema)
Off → uses SMA (slower but smoother).
On → uses EMA (reacts faster to recent changes).
Use Robust Stats (Median & MAD)? (use_robust)
Off → mean + standard deviation
On → median + MAD (less sensitive to a few insane spikes)
Useful for assets with occasional volume blow-ups.
⸻
2.2. Detection Mode
These inputs control how “unusual” is defined.
• Use Volume Change vs Previous Bar? (mode_change)
• Off (default) → uses absolute volume.
• On → uses abs(volume - volume ).
You then detect jumps in volume rather than absolute size.
Note: This is ignored if Z-Score mode is switched on (see below).
• Use Z-Score on Volume? (Overrides change) (mode_zscore)
• Off → high volume when raw value exceeds the upper band.
• On → computes z-score = (value − center) / spread and flags a bar as high when z ≥ multiplier.
Z-score mode can be combined with robust stats for more stable thresholds.
• Min Volume vs Avg (Filter) (min_rel_mult)
An extra filter to ignore tiny-volume bars that are statistically “weird” but not meaningful.
• 0.0 → no filter (all stats-based candidates allowed).
• 1.0 → high-volume bar must also be at least equal to average volume.
• 1.5 → bar must be ≥ 1.5 × average volume.
• Skip First N Bars (from start of chart) (skip_open_bars)
Skips the first N bars of the chart when evaluating high-volume conditions.
This is mostly a safety / cosmetic option to avoid weird behavior on very early bars or backfill.
⸻
2.3. Visuals
• Show Volume Bands? (show_bands)
• If on, plots:
• Upper band (upper)
• Lower band (lower)
• Center line (vol_center)
These are plotted on the same pane as the script (usually the price chart).
• Also Highlight Background? (use_bg)
• If on, fills the background on high-volume bars with High-Vol Background.
• High-Vol Bar Transparency (0–100) (bar_transp)
Controls the opacity of the high-volume bar colors (up / down).
• 0 → fully opaque
• 100 → fully transparent (no visible effect)
• Up Color (upColor) / Down Color (dnColor)
• Regular bar colors (non high-volume) for up and down bars.
• Up High-Vol Base Color (upHighVolBase) / Down High-Vol Base Color (dnHighVolBase)
Base colors used for high-volume up/down bars. Transparency is applied on top of these via bar_transp.
• High-Vol Background (bgHighVolColor)
Background color used when Also Highlight Background? is enabled.
⸻
3. What gets colored and how
• Bar color (barcolor)
• Up bar:
• High volume → Up High-Vol Color
• Normal volume → Up Color
• Down bar:
• High volume → Down High-Vol Color
• Normal volume → Down Color
• Flat bar → neutral gray
• Background color (bgcolor)
• If Also Highlight Background? is on, high-volume bars get High-Vol Background.
• Otherwise, background is unchanged.
⸻
4. Alerts
The indicator exposes three alert conditions:
• High Volume Bar
Triggers whenever is_high is true (up or down).
• High Volume Up Bar
Triggers only when is_high is true and the bar closed up (close > open).
• High Volume Down Bar
Triggers only when is_high is true and the bar closed down (close < open).
You can use these in TradingView’s “Create Alert” dialog to:
• Get notified of potential breakout / exhaustion bars.
• Trigger webhook events for bots / custom infra.
⸻
5. Recommended presets
5.1. “Classic” high-volume detector (closest to original)
• Lookback: 150–200
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: off
• Use Robust Stats?: off
• Use Volume Change vs Previous Bar?: off
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.0–1.0
Behavior: Flags bars whose volume is notably above the recent average (plus a bit of noise filtering), same spirit as your initial implementation.
⸻
5.2. Volatility-aware (Z-score) mode
• Lookback: 100–200
• StdDev / Z-Score Multiplier: 1.5–2.0
• Use EMA Instead of SMA?: on
• Use Robust Stats?: on (if asset has huge spikes)
• Use Volume Change vs Previous Bar?: off (ignored anyway in z-score mode)
• Use Z-Score on Volume?: on
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Flags bars that are “statistically extreme” relative to recent volume behavior, not just absolutely large. Good for assets where baseline volume drifts over time.
⸻
5.3. “Wake-up bar” (volume acceleration)
• Lookback: 50–100
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: on
• Use Robust Stats?: optional
• Use Volume Change vs Previous Bar?: on
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Emphasis on sudden increases in volume rather than absolute size – useful to catch “first active bar” after a quiet period.
⸻
6. Limitations / notes
• Time-of-day effects
The script currently treats the entire chart as one continuous “session”. On 24/7 markets (crypto) this is fine. For regular-session assets (equities, futures), volume naturally spikes at open/close; you may want to:
• Use a shorter Lookback, or
• Add a session-aware filter in a future iteration.
• Illiquid symbols
On very low-liquidity symbols, robust stats (Use Robust Stats) and a non-zero Min Volume vs Avg can help avoid “everything looks extreme” problems.
• Overlay behavior
overlay = true means:
• Bars are recolored on the price pane.
• Volume bands are also drawn on the price pane if enabled.
If you want a dedicated panel for the bands, duplicate the logic in a separate script with overlay = false.
Reversal Correlation Pressure [OmegaTools]Reversal Correlation Pressure is a quantitative regime-detection and signal-filtering framework designed to enhance both reversal timing and breakout validation across intraday and multi-session markets.
It is built for discretionary and systematic traders who require a statistically grounded filter capable of adapting to changing market conditions in real time.
1. Purpose and Overview
Market conditions constantly rotate through phases of expansion, contraction, trend persistence, and noise-driven mean reversion. Many strategies break down not because the signal is wrong, but because the regime is unsuitable.
This indicator solves that structural problem.
The tool measures the evolving correlation relationship between highs and lows — a robust proxy for how “organized” or “fragmented” price discovery currently is — and transforms it into a regime pressure reading. This reading is then used as the core variable to validate or filter reversal and breakout opportunities.
Combined with an internal performance-based filter that learns from its past signals, the indicator becomes a dynamic decision engine: it highlights only the signals that statistically perform best under the current market regime.
2. Core Components
2.1 Correlation-Based Regime Mapping
The relationship between highs and lows contains valuable information about market structure:
High correlation generally corresponds to coherent, directional markets where momentum and breakouts tend to prevail.
Low or unstable correlation often appears in overlapping, rotational phases where price oscillates and mean-reversion behavior dominates.
The indicator continuously evaluates this correlation, normalizes it statistically, and displays it as a pressure histogram:
Higher values indicate regimes favorable to trend continuation or momentum breakouts.
Lower values indicate regimes where reversals, pullbacks, and fade setups historically perform better.
This regime mapping is the foundation upon which the adaptive filter operates.
2.2 Reversal Stress & Breakout Stress Signaling
Raw directional opportunities are identified using statistically significant deviations from short-term equilibrium (overbought/oversold dynamics).
However, unlike traditional mean-reversion or breakout tools, signals here are not automatically taken. They must first be validated by the regime framework and then compared against the performance of similar past setups.
This dual evaluation sharply reduces the noise associated with reversal attempts during strong trends, while also preventing breakout attempts during choppy, anti-directional conditions.
2.3 Adaptive Regime-Selection Backtester
A key innovation of this indicator is its embedded micro-backtester, which continuously tracks how reversal or breakout signals have performed under each correlation regime.
The system evaluates two competing hypotheses:
Signals perform better during high-correlation regimes.
Signals perform better during low-correlation or neutral regimes.
For each new trigger, the indicator looks back at a rolling sample of past setups and measures short-term performance under both regimes. It then automatically selects the regime that currently demonstrates the superior historical edge.
In other words, the indicator:
Learns from recent market behavior
Determines which regime supports reversals
Determines which regime supports breakouts
Applies the optimal filter in real time
Highlights only the signals that historically outperformed under similar conditions
This creates a dynamic, statistically supervised approach to signal filtering — a substantial improvement over static or fixed-threshold systems.
2.4 Visual Components
To support rapid decision-making:
Correlation Pressure Histogram:
Encodes regime strength through a gradient-based color system, transitioning from neutral contexts into strong structural phases.
Directional Markers:
Visual arrows appear when a signal passes all filters and conditions.
Bar Coloring:
Bars can optionally be recolored to reflect active bullish or bearish bias after the adaptive filter approves a signal.
These components integrate seamlessly to give the trader a concise but complete view of the underlying conditions.
3. How to Use This Indicator
3.1 Identifying Regimes
The histogram is the anchor:
High, brightly colored columns suggest trend-friendly behavior where breakout alignment and directional follow-through have historically been stronger.
Low or muted columns suggest mean-reversion contexts where counter-trend opportunities and reversal setups gain reliability.
3.2 Filtering Signals
The indicator automatically decides whether a reversal or breakout trigger should be respected based on:
the current correlation regime,
the learned performance of recent signals under similar conditions, and
the directional stress detected in price.
The user does not need to adjust anything manually.
3.3 Integration with Other Tools
This indicator works best when combined with:
VWAP or session levels
Market internals and breadth metrics
Volume, order flow, or delta-based tools
Local structural frameworks (support/resistance, liquidity highs and lows)
Its strength is in telling you when your other signals matter and when they should be ignored.
4. Strengths of the Framework
Automatically adapts to changing micro-regimes
Reduces false reversals during strong trends
Avoids false breakouts in overlapping, rotational markets
Learns from recent historical performance
Provides a statistically driven confirmation layer
Works on all liquid assets and timeframes
Suitable for both discretionary and automated environments
5. Disclaimer
This indicator is provided strictly for educational and analytical purposes.
It does not constitute trading advice, investment guidance, or a recommendation to buy or sell any financial instrument.
Past performance of any statistical filter or adaptive method does not guarantee future results.
All trading involves significant risk, and users are responsible for their own decisions and risk management.
By using this indicator, you acknowledge that you are fully responsible for your trading activity.
